Abstract
There has been a decline in sales at Maestro Jakarta Cafe & Space due to a lot of competition and not optimal management of transaction data so that innovation is needed to increase sales. This study aims to compare the performance and results of the Apriori and FP-Growth algorithms in analyzing sales transaction data to determine the optimal sales strategy. This research uses the Apriori and FP-Growth methods to analyze sales transaction data by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM). The data used is product sales transaction data from November 2023 to April 2024. The results of performance comparisons in processing time speed and memory usage that have been carried out show that in processing time speed the FP-Growth algorithm is slightly faster than the Apriori algorithm while in the use of memory capacity the Apriori algorithm requires a larger memory capacity than the memory capacity used by the FP-Growth algorithm. This shows that the performance of the FP-Growth algorithm is better than the Apriori algorithm. The analysis results of the Apriori and FP-Growth algorithms on sales transaction data using a minimum support value of 1% and a minimum confidence value of 100% resulted in 22 association rules. Both algorithms produce identical rules, with the only difference being the occurrence index. The results of this analysis can be used by Maestro Jakarta Cafe & Space in determining sales strategies.
Published Version
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